{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据加载成功！\n",
      "\n",
      "开始期末分析：\n",
      "成绩分布统计：\n",
      "不及格    12\n",
      "及格      0\n",
      "中等      9\n",
      "良好      9\n",
      "优秀      0\n",
      "Name: 分数区间, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "及格率：60.00%\n",
      "平均分：60.63\n",
      "\n",
      "单题得分分析：\n",
      "选择题1 - 平均分: 1.13, 最高分: 2, 最低分: 0, 类型: 选择题（较易）\n",
      "选择题2 - 平均分: 1.27, 最高分: 2, 最低分: 0, 类型: 选择题（较易）\n",
      "选择题3 - 平均分: 0.93, 最高分: 2, 最低分: 0, 类型: 选择题（较难）\n",
      "选择题4 - 平均分: 1.47, 最高分: 2, 最低分: 0, 类型: 选择题（较易）\n",
      "选择题5 - 平均分: 1.07, 最高分: 2, 最低分: 0, 类型: 选择题（较易）\n",
      "选择题6 - 平均分: 1.33, 最高分: 2, 最低分: 0, 类型: 选择题（较易）\n",
      "选择题7 - 平均分: 1.33, 最高分: 2, 最低分: 0, 类型: 选择题（较易）\n",
      "选择题8 - 平均分: 1.07, 最高分: 2, 最低分: 0, 类型: 选择题（较易）\n",
      "选择题9 - 平均分: 1.47, 最高分: 2, 最低分: 0, 类型: 选择题（较易）\n",
      "选择题10 - 平均分: 1.13, 最高分: 2, 最低分: 0, 类型: 选择题（较易）\n",
      "简答题1 - 平均分: 5.63, 最高分: 9, 最低分: 0, 类型: 简答题（较易）\n",
      "简答题2 - 平均分: 5.50, 最高分: 10, 最低分: 0, 类型: 简答题（较易）\n",
      "简答题3 - 平均分: 5.40, 最高分: 9, 最低分: 1, 类型: 简答题（较易）\n",
      "简答题4 - 平均分: 6.73, 最高分: 10, 最低分: 2, 类型: 简答题（较易）\n",
      "简答题5 - 平均分: 6.13, 最高分: 10, 最低分: 0, 类型: 简答题（较易）\n",
      "编程题1 - 平均分: 9.23, 最高分: 15, 最低分: 0, 类型: 其他题（较难）\n",
      "编程题2 - 平均分: 9.80, 最高分: 15, 最低分: 1, 类型: 其他题（较难）\n",
      "选择题总分 - 平均分: 12.20, 最高分: 20, 最低分: 2, 类型: 其他题（较易）\n",
      "简答题总分 - 平均分: 29.40, 最高分: 46, 最低分: 10, 类型: 其他题（较易）\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "学生成绩排名：\n",
      "    ID    姓名  总分    排名   分类\n",
      "10  11  学生11  85   1.0   良好\n",
      "6    7   学生7  84   2.0   良好\n",
      "25  26  学生26  83   3.0   良好\n",
      "12  13  学生13  83   3.0   良好\n",
      "26  27  学生27  82   5.0   良好\n",
      "0    1   学生1  81   6.0   良好\n",
      "27  28  学生28  81   6.0   良好\n",
      "20  21  学生21  81   6.0   良好\n",
      "14  15  学生15  80   9.0   良好\n",
      "29  30  学生30  79  10.0   中等\n",
      "22  23  学生23  78  11.0   中等\n",
      "8    9   学生9  76  12.0   中等\n",
      "18  19  学生19  76  12.0   中等\n",
      "4    5   学生5  74  14.0   中等\n",
      "16  17  学生17  73  15.0   中等\n",
      "24  25  学生25  73  15.0   中等\n",
      "28  29  学生29  70  17.0   中等\n",
      "2    3   学生3  70  17.0   中等\n",
      "5    6   学生6  38  19.0  不及格\n",
      "21  22  学生22  36  20.0  不及格\n",
      "15  16  学生16  36  20.0  不及格\n",
      "19  20  学生20  36  20.0  不及格\n",
      "17  18  学生18  36  20.0  不及格\n",
      "13  14  学生14  36  20.0  不及格\n",
      "23  24  学生24  33  25.0  不及格\n",
      "1    2   学生2  33  25.0  不及格\n",
      "11  12  学生12  32  27.0  不及格\n",
      "7    8   学生8  32  27.0  不及格\n",
      "3    4   学生4  31  29.0  不及格\n",
      "9   10  学生10  31  29.0  不及格\n",
      "\n",
      "教学改进建议：\n",
      "选择题第 3 题学生整体得分较低，建议加强该知识点的教学。\n",
      "其他题第 1 题学生整体得分较低，建议补充教学内容。\n",
      "其他题第 2 题学生整体得分较低，建议补充教学内容。\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "学生 1 的成绩分析报告：\n",
      "姓名：学生1\n",
      "总分：81\n",
      "题1：得分 2（正确）\n",
      "题2：得分 2（正确）\n",
      "题3：得分 0\n",
      "  本题为选择题，回答错误，建议复习相关知识点。\n",
      "题4：得分 2（正确）\n",
      "题5：得分 2（正确）\n",
      "题6：得分 2（正确）\n",
      "题7：得分 2（正确）\n",
      "题8：得分 2（正确）\n",
      "题9：得分 0\n",
      "  本题为选择题，回答错误，建议复习相关知识点。\n",
      "题10：得分 2（正确）\n",
      "题11：得分 8（正确）\n",
      "题12：得分 7（正确）\n",
      "题13：得分 9（正确）\n",
      "题14：得分 6（正确）\n",
      "题15：得分 10（正确）\n",
      "题16：得分 10（正确）\n",
      "题17：得分 15（正确）\n",
      "题18：得分 16（正确）\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# 设置 Matplotlib 支持中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "class GradeAnalysis:\n",
    "    def __init__(self, file_path):\n",
    "        \"\"\"\n",
    "        初始化，读取 Excel 文件并处理数据\n",
    "        \"\"\"\n",
    "        try:\n",
    "            self.data = pd.read_excel(file_path, engine='openpyxl')\n",
    "            self.students = self._process_data()\n",
    "            print(\"数据加载成功！\")\n",
    "        except Exception as e:\n",
    "            print(f\"数据加载失败，请检查文件路径或文件格式。错误信息：{e}\")\n",
    "            exit()\n",
    "\n",
    "    def _process_data(self):\n",
    "        \"\"\"\n",
    "        处理 Excel 数据并存储为字典格式\n",
    "        \"\"\"\n",
    "        students = {}\n",
    "        for _, row in self.data.iterrows():\n",
    "            student_id = str(row['ID'])  # 学生ID\n",
    "            students[student_id] = {\n",
    "                \"name\": row['姓名'],  # 学生姓名\n",
    "                \"scores\": row[2:-3].tolist(),  # 各题得分（去除选择题总分、简答题总分、编程题总分和总分列）\n",
    "                \"total\": row['总分']  # 总分\n",
    "            }\n",
    "        return students\n",
    "\n",
    "    def grade_distribution(self):\n",
    "        \"\"\"\n",
    "        统计成绩分布：成绩区间人数\n",
    "        \"\"\"\n",
    "        bins = [0, 60, 70, 80, 90, 100]  # 分数区间\n",
    "        labels = ['不及格', '及格', '中等', '良好', '优秀']\n",
    "        self.data['分数区间'] = pd.cut(self.data['总分'], bins=bins, labels=labels, right=False)\n",
    "        distribution = self.data['分数区间'].value_counts().sort_index()\n",
    "        return distribution\n",
    "\n",
    "    def pass_rate_and_average(self):\n",
    "        \"\"\"\n",
    "        统计及格率和平均分\n",
    "        \"\"\"\n",
    "        total_students = len(self.data)\n",
    "        passed_students = len(self.data[self.data['总分'] >= 60])\n",
    "        pass_rate = passed_students / total_students * 100\n",
    "        average_score = self.data['总分'].mean()\n",
    "        return pass_rate, average_score\n",
    "\n",
    "    def single_question_analysis(self):\n",
    "        \"\"\"\n",
    "        单题得分分析：统计每道题的平均分、最高分、最低分，并区分题型判断得分情况\n",
    "        \"\"\"\n",
    "        question_columns = self.data.columns[2:-3]  # 去除选择题总分、简答题总分、编程题总分和总分列\n",
    "        analysis = {}\n",
    "        for index, question in enumerate(question_columns):\n",
    "            if pd.api.types.is_numeric_dtype(self.data[question]):\n",
    "                average_score = self.data[question].mean()\n",
    "                max_score = self.data[question].max()\n",
    "                min_score = self.data[question].min()\n",
    "                if index < 10:  # 假设前 10 题为选择题，每题 2 分\n",
    "                    question_type = \"选择题（较难）\" if average_score < 1 else \"选择题（较易）\"\n",
    "                elif index < 15:  # 假设接下来 5 题为简答题，每题 10 分\n",
    "                    question_type = \"简答题（较难）\" if average_score < 5 else \"简答题（较易）\"\n",
    "                else:  # 其他题\n",
    "                    question_type = \"其他题（较难）\" if average_score < 10 else \"其他题（较易）\"\n",
    "                analysis[question] = {\n",
    "                    \"average\": average_score,\n",
    "                    \"max\": max_score,\n",
    "                    \"min\": min_score,\n",
    "                    \"type\": question_type\n",
    "                }\n",
    "        return analysis\n",
    "\n",
    "    def rank_students(self):\n",
    "        \"\"\"\n",
    "        按总分排名，并分类统计\n",
    "        \"\"\"\n",
    "        self.data['排名'] = self.data['总分'].rank(ascending=False, method='min')  # 按总分排名\n",
    "        bins = [0, 60, 70, 80, 90, 100]  # 分数区间\n",
    "        labels = ['不及格', '及格', '中等', '良好', '优秀']\n",
    "        self.data['分类'] = pd.cut(self.data['总分'], bins=bins, labels=labels, right=False)\n",
    "        rank_data = self.data.sort_values(by='总分', ascending=False)[['ID', '姓名', '总分', '排名', '分类']]\n",
    "        return rank_data\n",
    "\n",
    "    def generate_student_feedback(self, student_id, save_path=None):\n",
    "        \"\"\"\n",
    "        学生个性化反馈报告生成，并绘制每道题得分情况的折线图\n",
    "        \"\"\"\n",
    "        if student_id in self.students:\n",
    "            student_info = self.students[student_id]\n",
    "            feedback = f\"学生 {student_id} 的成绩分析报告：\\n\"\n",
    "            feedback += f\"姓名：{student_info['name']}\\n\"\n",
    "            feedback += f\"总分：{student_info['total']}\\n\"\n",
    "            for i, score in enumerate(student_info['scores']):\n",
    "                question_name = f\"题{i + 1}\"\n",
    "                if i < 10 and score >= 1:  # 根据选择题分值调整判断标准，判断为正确\n",
    "                    feedback += f\"{question_name}：得分 {score}（正确）\\n\"\n",
    "                elif 10 <= i < 15 and score >= 5:  # 简答题判断为正确\n",
    "                    feedback += f\"{question_name}：得分 {score}（正确）\\n\"\n",
    "                elif i >= 15 and score >= 10:  # 其他题判断为正确\n",
    "                    feedback += f\"{question_name}：得分 {score}（正确）\\n\"\n",
    "                else:\n",
    "                    feedback += f\"{question_name}：得分 {score}\\n\"\n",
    "                    if i < 10 and score < 1:  # 根据选择题分值调整判断标准\n",
    "                        feedback += f\"  本题为选择题，回答错误，建议复习相关知识点。\\n\"\n",
    "                    elif 10 <= i < 15 and score < 5:  # 简答题判断\n",
    "                        feedback += f\"  本题为简答题，回答情况不佳，建议加强复习。\\n\"\n",
    "                    elif i >= 15 and score < 10:  # 其他题判断\n",
    "                        feedback += f\"  本题回答有欠缺，建议进一步学习。\\n\"\n",
    "\n",
    "            # 绘制每道题得分情况的折线图\n",
    "            plt.figure(figsize=(10, 6))\n",
    "            plt.plot(range(1, len(student_info['scores']) + 1), student_info['scores'], marker='o', label=f'学生 {student_id}')\n",
    "            plt.xlabel('题目编号')\n",
    "            plt.ylabel('得分')\n",
    "            plt.title(f'学生 {student_id} 的每道题得分情况')\n",
    "            plt.xticks(range(1, len(student_info['scores']) + 1))\n",
    "            plt.legend()\n",
    "            plt.tight_layout()\n",
    "\n",
    "            if save_path:\n",
    "                plt.savefig(os.path.join(save_path, f\"学生{student_id}_题目得分折线图.png\"), bbox_inches='tight')\n",
    "                plt.close()\n",
    "            else:\n",
    "                plt.show()\n",
    "\n",
    "            return feedback\n",
    "        else:\n",
    "            return f\"未找到ID为 {student_id} 的学生信息。\"\n",
    "\n",
    "    def generate_teaching_improvement(self):\n",
    "        \"\"\"\n",
    "        教学改进建议生成\n",
    "        \"\"\"\n",
    "        question_scores = []\n",
    "        for question in self.data.columns[2:-3]:  # 去除选择题总分、简答题总分、编程题总分和总分列\n",
    "            if pd.api.types.is_numeric_dtype(self.data[question]):\n",
    "                scores = self.data[question]\n",
    "                average_score = scores.mean()\n",
    "                question_scores.append(average_score)\n",
    "\n",
    "        improvement_suggestions = []\n",
    "        for i, score in enumerate(question_scores):\n",
    "            if i < 10 and score < 1:  # 根据选择题分值调整判断标准\n",
    "                improvement_suggestions.append(f\"选择题第 {i + 1} 题学生整体得分较低，建议加强该知识点的教学。\")\n",
    "            elif 10 <= i < 15 and score < 5:  # 简答题判断\n",
    "                improvement_suggestions.append(f\"简答题第 {i - 9} 题学生整体得分较低，建议详细讲解。\")\n",
    "            elif i >= 15 and score < 10:  # 其他题判断\n",
    "                improvement_suggestions.append(f\"其他题第 {i - 14} 题学生整体得分较低，建议补充教学内容。\")\n",
    "\n",
    "        return improvement_suggestions\n",
    "\n",
    "    def plot_grade_distribution(self, save_path=None):\n",
    "        \"\"\"\n",
    "        生成成绩分布柱状图\n",
    "        \"\"\"\n",
    "        distribution = self.grade_distribution()\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        bars = plt.bar(distribution.index, distribution.values, color='skyblue')\n",
    "        plt.xlabel('分数区间', fontsize=12)\n",
    "        plt.ylabel('人数', fontsize=12)\n",
    "        plt.title('班级成绩分布柱状图', fontsize=14)\n",
    "        plt.xticks(distribution.index, labels=['不及格', '及格', '中等', '良好', '优秀'], fontsize=10)\n",
    "        plt.yticks(fontsize=10)\n",
    "        plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "        # 在柱状图上显示具体数值\n",
    "        for bar in bars:\n",
    "            height = bar.get_height()\n",
    "            plt.text(bar.get_x() + bar.get_width() / 2, height, f'{int(height)}', ha='center', va='bottom')\n",
    "\n",
    "        if save_path:\n",
    "            plt.savefig(os.path.join(save_path, \"成绩分布柱状图.png\"), bbox_inches='tight')\n",
    "            plt.close()\n",
    "        else:\n",
    "            plt.show()\n",
    "\n",
    "    def plot_question_type_average_distribution(self, save_path=None):\n",
    "        \"\"\"\n",
    "        生成各题型得分平均分布饼状图\n",
    "        \"\"\"\n",
    "        question_analysis = self.single_question_analysis()\n",
    "        types = {}\n",
    "        for question in question_analysis:\n",
    "            question_type = question_analysis[question]['type']\n",
    "            if question_type in types:\n",
    "                types[question_type].append(question_analysis[question]['average'])\n",
    "            else:\n",
    "                types[question_type] = [question_analysis[question]['average']]\n",
    "        for question_type in types:\n",
    "            types[question_type] = sum(types[question_type]) / len(types[question_type])\n",
    "\n",
    "        # 定义颜色列表\n",
    "        colors = ['lightcoral', 'lightgreen', 'lightskyblue', 'gold', 'lightpink', 'lightyellow', 'lightcyan']\n",
    "\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        wedges, texts, autotexts = plt.pie(\n",
    "            types.values(), \n",
    "            labels=types.keys(),  # 使用题型作为标签\n",
    "            autopct='%1.1f%%', \n",
    "            startangle=90, \n",
    "            colors=colors[:len(types)],  # 使用定义的颜色列表\n",
    "            textprops={'fontsize': 10}\n",
    "        )\n",
    "        plt.title('各题型得分平均分布饼状图', fontsize=14)\n",
    "        plt.legend(wedges, types.keys(), title=\"题型\", loc=\"center left\", bbox_to_anchor=(1, 0, 0.5, 1), handlelength=0)\n",
    "\n",
    "        if save_path:\n",
    "            plt.savefig(os.path.join(save_path, \"题型得分分布饼状图.png\"), bbox_inches='tight')\n",
    "            plt.close()\n",
    "        else:\n",
    "            plt.show()\n",
    "\n",
    "    def download_all_results(self, download_path):\n",
    "        \"\"\"\n",
    "        下载所有分析结果\n",
    "        \"\"\"\n",
    "        if not os.path.exists(download_path):\n",
    "            os.makedirs(download_path)\n",
    "\n",
    "        # 保存总体分析报告\n",
    "        self.generate_overall_analysis_report(save_path=os.path.join(download_path, \"总体分析报告.txt\"))\n",
    "\n",
    "        # 保存成绩分布柱状图\n",
    "        self.plot_grade_distribution(save_path=download_path)\n",
    "\n",
    "        # 保存各题型得分平均分布饼状图\n",
    "        self.plot_question_type_average_distribution(save_path=download_path)\n",
    "\n",
    "        print(f\"所有分析结果已保存到 {download_path}\")\n",
    "\n",
    "\n",
    "# 主程序\n",
    "if __name__ == \"__main__\":\n",
    "    file_path = input(\"请输入 Excel 文件路径：\")\n",
    "    analysis = GradeAnalysis(file_path)\n",
    "\n",
    "    # 1. 期末分析\n",
    "    print(\"\\n开始期末分析：\")\n",
    "    print(\"成绩分布统计：\")\n",
    "    print(analysis.grade_distribution())\n",
    "    analysis.plot_grade_distribution()\n",
    "\n",
    "    pass_rate, average_score = analysis.pass_rate_and_average()\n",
    "    print(f\"\\n及格率：{pass_rate:.2f}%\")\n",
    "    print(f\"平均分：{average_score:.2f}\")\n",
    "\n",
    "    print(\"\\n单题得分分析：\")\n",
    "    single_question_stats = analysis.single_question_analysis()\n",
    "    for question, stats in single_question_stats.items():\n",
    "        print(f\"{question} - 平均分: {stats['average']:.2f}, 最高分: {stats['max']}, 最低分: {stats['min']}, 类型: {stats['type']}\")\n",
    "\n",
    "    analysis.plot_question_type_average_distribution()\n",
    "\n",
    "    print(\"\\n学生成绩排名：\")\n",
    "    print(analysis.rank_students())\n",
    "\n",
    "    print(\"\\n教学改进建议：\")\n",
    "    for suggestion in analysis.generate_teaching_improvement():\n",
    "        print(suggestion)\n",
    "\n",
    "    # 2. 学生查询功能\n",
    "    while True:\n",
    "        student_id = input(\"\\n请输入学生编号查询成绩和个性化分析（输入 'exit' 退出）：\")\n",
    "        if student_id.lower() == 'exit':\n",
    "            break\n",
    "        feedback = analysis.generate_student_feedback(student_id)\n",
    "        if feedback:\n",
    "            print(feedback)\n",
    "        else:\n",
    "            print(f\"未找到ID为 {student_id} 的学生信息。\")\n",
    "\n",
    "    # 3. 统一下载所有结果\n",
    "    download = input(\"\\n是否下载所有分析结果？（输入 'y' 下载，其他键跳过）：\")\n",
    "    if download.lower() == 'y':\n",
    "        download_path = input(\"请输入保存路径（例如：C:\\\\Users\\\\YourName\\\\Desktop\\\\）：\")\n",
    "        analysis.download_all_results(download_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据加载成功！\n",
      "学生1 的最终成绩计算结果为：81.53\n",
      "学生2 的最终成绩计算结果为：68.91\n",
      "学生3 的最终成绩计算结果为：81.51\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    姓名     成绩 等级 等级详细   分数区间\n",
      "0  学生1  81.53  B   B-  80-89\n",
      "1  学生2  68.91  D    D  60-69\n",
      "2  学生3  81.51  B   B-  80-89\n",
      "班级平均分: 77.32\n",
      "请输入有效的学生编号（数字）。\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import zipfile\n",
    "\n",
    "# 设置 Matplotlib 的字体为支持中文的字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "\n",
    "def calculate_final_score(file_path):\n",
    "    \"\"\"\n",
    "    根据 Excel 文件中的成绩和百分比计算最终结果\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # 读取 Excel 文件\n",
    "        df = pd.read_excel(file_path, engine='openpyxl', header=None)\n",
    "        print(\"数据加载成功！\")\n",
    "    except Exception as e:\n",
    "        print(f\"数据加载失败，请检查文件路径或文件格式。错误信息：{e}\")\n",
    "        return None\n",
    "\n",
    "    # 定义所需列\n",
    "    required_columns = ['作业', '实验', '课堂练习', '期中考试', '期末考试']\n",
    "\n",
    "    # 初始化学生数据\n",
    "    students = {}\n",
    "    current_student = None\n",
    "\n",
    "    # 遍历每一行，识别学生数据\n",
    "    for index, row in df.iterrows():\n",
    "        if pd.notna(row[0]) and '学生' in str(row[0]):  # 动态识别学生\n",
    "            current_student = row[0]  # 当前学生\n",
    "            students[current_student] = {'data': [], 'percentages': {}, 'final_exam_scores': []}\n",
    "        elif row[0] == '合计':\n",
    "            # 提取当前学生的百分比信息\n",
    "            percentages = {}\n",
    "            for i, col in enumerate(required_columns):\n",
    "                value = row[i + 2]  # 从第 3 列开始是百分比数据\n",
    "                if isinstance(value, str) and '%' in value:\n",
    "                    percentages[col] = float(value.strip('%')) / 100  # 将百分比字符串转换为小数\n",
    "                elif pd.notna(value):\n",
    "                    percentages[col] = float(value)\n",
    "                else:\n",
    "                    percentages[col] = 0.0  # 如果值为空，设为 0\n",
    "            students[current_student]['percentages'] = percentages\n",
    "        elif row[0] in ['序号', '']:\n",
    "            continue  # 跳过标题行和空行\n",
    "        else:\n",
    "            # 检查是否有期末成绩（与“目标1”在同一行）\n",
    "            if pd.notna(row[7]):  # 期末成绩在第 8 列（索引 7）\n",
    "                students[current_student]['final_exam_scores'].append(float(row[7]))\n",
    "            # 添加当前学生的成绩数据\n",
    "            if pd.notna(row[2]):  # 检查成绩数据是否有效\n",
    "                students[current_student]['data'].append(row)\n",
    "\n",
    "    # 计算每个学生的最终成绩\n",
    "    final_scores = {}\n",
    "    for student, info in students.items():\n",
    "        data = info['data']\n",
    "        percentages = info['percentages']\n",
    "        final_exam_scores = info.get('final_exam_scores', [])\n",
    "\n",
    "        # 初始化最终结果\n",
    "        final_score = 0\n",
    "\n",
    "        # 计算作业、实验、课堂练习、期中考试的加权分数\n",
    "        for row in data:\n",
    "            weighted_score = 0\n",
    "            for i, col in enumerate(required_columns[:-1]):  # 排除期末考试\n",
    "                score = row[i + 2]  # 从第 3 列开始是成绩数据\n",
    "                if pd.notna(score):\n",
    "                    weighted_score += score * percentages.get(col, 0)  # 使用百分比权重\n",
    "            final_score += weighted_score * row[6]  # 乘以成绩比例\n",
    "\n",
    "        # 检查期末成绩是否有多个数值\n",
    "        if len(final_exam_scores) == 1:\n",
    "            final_exam_score = final_exam_scores[0]\n",
    "        elif len(final_exam_scores) > 1:\n",
    "            final_exam_score = sum(final_exam_scores)\n",
    "        else:\n",
    "            final_exam_score = float(input(f\"请输入 {student} 的期末总成绩：\"))\n",
    "\n",
    "        # 计算剩余百分比\n",
    "        total_percentage_used = sum(percentages.get(col, 0) for col in required_columns[:-1])  # 排除期末考试\n",
    "        remaining_percentage = 1 - total_percentage_used\n",
    "        if remaining_percentage < 0:\n",
    "            print(f\"警告：{student} 的剩余百分比为负数，期末成绩将不计入最终成绩！\")\n",
    "            remaining_percentage = 0\n",
    "\n",
    "        # 将期末成绩与其比例相乘\n",
    "        final_exam_weighted = final_exam_score * remaining_percentage\n",
    "        final_score += final_exam_weighted\n",
    "\n",
    "        final_scores[student] = final_score\n",
    "        print(f\"{student} 的最终成绩计算结果为：{final_score:.2f}\")\n",
    "\n",
    "    # 将最终成绩保存到 DataFrame\n",
    "    final_df = pd.DataFrame(list(final_scores.items()), columns=['姓名', '成绩'])\n",
    "    return final_df\n",
    "\n",
    "\n",
    "def analyze_scores(final_df):\n",
    "    \"\"\"\n",
    "    对最终成绩进行分析和可视化\n",
    "    \"\"\"\n",
    "    # 定义分数区间和对应的等级\n",
    "    def get_grade(score):\n",
    "        if score < 60:\n",
    "            return 'F'\n",
    "        elif 60 <= score < 70:\n",
    "            return 'D'\n",
    "        elif 70 <= score < 80:\n",
    "            return 'C'\n",
    "        elif 80 <= score < 90:\n",
    "            return 'B'\n",
    "        elif 90 <= score <= 100:\n",
    "            return 'A'\n",
    "\n",
    "    def get_grade_detail(score):\n",
    "        if score < 60:\n",
    "            return 'F'\n",
    "        elif 60 <= score < 70:\n",
    "            return 'D'\n",
    "        elif 70 <= score < 80:\n",
    "            if score < 75:\n",
    "                return 'C-'\n",
    "            else:\n",
    "                return 'C+'\n",
    "        elif 80 <= score < 90:\n",
    "            if score < 85:\n",
    "                return 'B-'\n",
    "            else:\n",
    "                return 'B+'\n",
    "        elif 90 <= score <= 100:\n",
    "            if score < 95:\n",
    "                return 'A-'\n",
    "            else:\n",
    "                return 'A'\n",
    "\n",
    "    # 计算每个学生的等级\n",
    "    final_df['等级'] = final_df['成绩'].apply(get_grade)\n",
    "    final_df['等级详细'] = final_df['成绩'].apply(get_grade_detail)\n",
    "\n",
    "    # 计算班级平均分\n",
    "    average_score = final_df['成绩'].mean()\n",
    "\n",
    "    # 绘制班级成绩分布柱状图\n",
    "    def plot_score_distribution(df):\n",
    "        bins = [0, 60, 70, 80, 90, 100]\n",
    "        labels = ['0-59', '60-69', '70-79', '80-89', '90-100']\n",
    "        df['分数区间'] = pd.cut(df['成绩'], bins=bins, labels=labels, right=False)\n",
    "        score_distribution = df['分数区间'].value_counts().sort_index()\n",
    "\n",
    "        plt.figure(figsize=(10, 6))\n",
    "        plt.bar(score_distribution.index, score_distribution.values, color='skyblue')\n",
    "        plt.xlabel('分数区间')\n",
    "        plt.ylabel('人数')\n",
    "        plt.title('班级成绩分布柱状图')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    # 绘制班级成绩分布柱状图\n",
    "    plot_score_distribution(final_df)\n",
    "\n",
    "    # 绘制柱状图\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.bar(final_df['姓名'], final_df['成绩'], color='skyblue')\n",
    "    plt.axhline(y=average_score, color='r', linestyle='--', label=f'班级平均分: {average_score:.2f}')\n",
    "    plt.xlabel('学生')\n",
    "    plt.ylabel('分数')\n",
    "    plt.title('学生成绩柱状图')\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # 绘制散点图\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.scatter(final_df['姓名'], final_df['成绩'], color='blue', label='学生成绩')\n",
    "    plt.axhline(y=average_score, color='r', linestyle='--', label=f'班级平均分: {average_score:.2f}')\n",
    "    plt.xlabel('学生')\n",
    "    plt.ylabel('分数')\n",
    "    plt.title('学生成绩散点图')\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # 输出结果\n",
    "    print(final_df)\n",
    "    print(f\"班级平均分: {average_score:.2f}\")\n",
    "\n",
    "    # 保存结果到Excel文件（可选）\n",
    "    save_option = input(\"是否将结果保存到Excel文件？(y/n): \")\n",
    "    if save_option.lower() == 'y':\n",
    "        output_path = input(\"请输入保存路径（包括文件名，例如 output.xlsx）: \")\n",
    "        try:\n",
    "            final_df.to_excel(output_path, index=False)\n",
    "            print(f\"结果已保存到 {output_path}\")\n",
    "        except Exception as e:\n",
    "            print(f\"保存文件失败: {e}\")\n",
    "\n",
    "    # 导出单个学生成绩和散点图\n",
    "    student_id = input(\"请输入要导出的学生编号（输入0跳过）: \")\n",
    "    if student_id != '0':\n",
    "        try:\n",
    "            student_id = int(student_id)\n",
    "            student_data = final_df[final_df.index == student_id - 1]  # 假设学生编号从1开始\n",
    "            if not student_data.empty:\n",
    "                # 保存学生成绩到Excel文件\n",
    "                output_student_path = input(f\"请输入保存路径（包括文件名，例如 学生{student_id}_成绩.xlsx）: \")\n",
    "                student_data.to_excel(output_student_path, index=False)\n",
    "                print(f\"学生 {student_id} 的成绩已保存到 {output_student_path}\")\n",
    "\n",
    "                # 绘制并保存散点图\n",
    "                plt.figure(figsize=(8, 6))\n",
    "                plt.scatter(final_df['姓名'], final_df['成绩'], color='blue', label='学生成绩')\n",
    "                plt.scatter(student_data['姓名'], student_data['成绩'], color='red', label=f'学生{student_id}成绩')\n",
    "                plt.axhline(y=average_score, color='r', linestyle='--', label=f'班级平均分: {average_score:.2f}')\n",
    "                plt.xlabel('学生')\n",
    "                plt.ylabel('分数')\n",
    "                plt.title(f'学生成绩散点图（突出显示学生{student_id}）')\n",
    "                plt.xticks(rotation=90)\n",
    "                plt.legend()\n",
    "                plt.tight_layout()\n",
    "\n",
    "                # 保存散点图\n",
    "                scatter_plot_path = os.path.splitext(output_student_path)[0] + \"_散点图.png\"\n",
    "                plt.savefig(scatter_plot_path)\n",
    "                print(f\"散点图已保存到 {scatter_plot_path}\")\n",
    "\n",
    "                # 将Excel文件和散点图打包为ZIP文件\n",
    "                zip_path = os.path.splitext(output_student_path)[0] + \"_打包.zip\"\n",
    "                with zipfile.ZipFile(zip_path, 'w') as zipf:\n",
    "                    zipf.write(output_student_path, os.path.basename(output_student_path))\n",
    "                    zipf.write(scatter_plot_path, os.path.basename(scatter_plot_path))\n",
    "                print(f\"成绩和散点图已打包到 {zip_path}\")\n",
    "\n",
    "            else:\n",
    "                print(f\"未找到编号为 {student_id} 的学生。\")\n",
    "        except ValueError:\n",
    "            print(\"请输入有效的学生编号（数字）。\")\n",
    "        except Exception as e:\n",
    "            print(f\"保存文件失败: {e}\")\n",
    "\n",
    "\n",
    "# 主程序\n",
    "if __name__ == \"__main__\":\n",
    "    file_path = input(\"请输入 Excel 文件路径：\")\n",
    "    final_df = calculate_final_score(file_path)\n",
    "    if final_df is not None:\n",
    "        analyze_scores(final_df)"
   ]
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